24_transformers_time_series.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/TimeSeries/blob/main/24_transformers_time_series.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Probabilistic Time Series Forecasting with 🤗 Transformers\n",
        "\n",
        "## Introduction\n",
        "\n",
        "Time series forecasting is an essential scientific and business problem and as such has also seen a lot of innovation recently with the use of [deep learning based](https://dl.acm.org/doi/abs/10.1145/3533382) models in addition to the [classical methods](https://otexts.com/fpp3/). An important difference between classical methods like ARIMA and novel deep learning methods is the following.\n",
        "\n",
        "##  Probabilistic Forecasting\n",
        "\n",
        "Typically, classical methods are fitted on each time series in a dataset individually. These are often referred to as  \"single\" or \"local\" methods. However, when dealing with a large amount of time series for some applications, it is beneficial to train a \"global\" model on all available time series, which enables the model to learn latent representations from many different sources.\n",
        "\n",
        "Some classical methods are point-valued (meaning, they just output a single value per time step) and models are trained by minimizing an L2 or L1 type of loss with respect to the ground truth data. However, since forecasts are often used in some real-world decision making pipeline, even with humans in the loop, it is much more beneficial to provide the uncertainties of predictions. This is also called \"probabilistic forecasting\", as opposed to \"point forecasting\". This entails modeling a probabilistic distribution, from which one can sample.\n",
        "\n",
        "So in short, rather than training local point forecasting models, we hope to train **global probabilistic** models. Deep learning is a great fit for this, as neural networks can learn representations from several related time series as well as model the uncertainty of the data.\n",
        "\n",
        "It is common in the probabilistic setting to learn the future parameters of some chosen parametric distribution, like Gaussian or Student-T; or learn the conditional quantile function; or use the framework of Conformal Prediction adapted to the time series setting. The choice of method does not affect the modeling aspect and thus can be typically thought of as yet another hyperparameter. One can always turn a probabilistic model into a point-forecasting model, by taking empirical means or medians.\n",
        "\n",
        "## The Time Series Transformer\n",
        "\n",
        "In terms of modeling time series data which are sequential in nature, as one can imagine, researchers have come up with models which use Recurrent Neural Networks (RNN) like LSTM or GRU, or Convolutional Networks (CNN), and more recently Transformer based methods which fit naturally to the time series forecasting setting.\n",
        "\n",
        "In this blog post, we're going to leverage the vanilla Transformer [(Vaswani et al., 2017)](https://arxiv.org/abs/1706.03762) for the **univariate** probabilistic forecasting task (i.e. predicting each time series' 1-d distribution individually). The Encoder-Decoder Transformer is a natural choice for forecasting as it encapsulates several inductive biases nicely.\n",
        "\n",
        "To begin with, the use of an Encoder-Decoder architecture is helpful at inference time where typically for some logged data we wish to forecast some prediction steps into the future. This can be thought of as analogous to the text generation task where given some context, we sample the next token and pass it back into the decoder (also called \"autoregressive generation\"). Similarly here we can also, given some distribution type, sample from it to provide forecasts up until our desired prediction horizon. This is known as Greedy Sampling/Search and there is a great blog post about it [here](https://huggingface.co/blog/how-to-generate) for the NLP setting.\n",
        "\n",
        "Secondly, a Transformer helps us to train on time series data which might contain thousands of time points. It might not be feasible to input *all* the history of a time series at once to the model, due to the time- and memory constraints of the attention mechanism. Thus, one can consider some appropriate context window and sample this window and the subsequent prediction length sized window from the training data when constructing batches for stochastic gradient descent (SGD). The context sized window can be passed to the encoder and the prediction window to a *causal-masked* decoder. This means that the decoder can only look at previous time steps when learning the next value. This is equivalent to how one would train a vanilla Transformer for machine translation, referred to as \"teacher forcing\".\n",
        "\n",
        "Another benefit of Transformers over the other architectures is that we can incorporate missing values (which are common in the time series setting) as an additional mask to the encoder or decoder and still train without resorting to in-filling or imputation. This is equivalent to the `attention_mask` of models like BERT and GPT-2 in the Transformers library, to not include padding tokens in the computation of the attention matrix.\n",
        "\n",
        "A drawback of the Transformer architecture is the limit to the sizes of the context and prediction windows because of the quadratic compute and memory requirements of the vanilla Transformer, see [Tay et al., 2020](https://arxiv.org/abs/2009.06732). Additionally, since the Transformer is a powerful architecture, it might overfit or learn spurious correlations much more easily compared to other [methods](https://openreview.net/pdf?id=D7YBmfX_VQy).\n",
        "\n",
        "The 🤗 Transformers library comes with a vanilla probabilistic time series Transformer model, simply called the [Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer). In the sections below, we'll show how to train such a model on a custom dataset.\n",
        "\n"
      ]
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
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      ],
      "source": [
        "!pip install -q transformers datasets evaluate accelerate gluonts ujson"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eaebea67",
      "metadata": {
        "id": "eaebea67"
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      "source": [
        "We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely."
      ]
    },
    {
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      "id": "98c2cf63",
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        "id": "98c2cf63"
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      "source": [
        "from transformers.utils import send_example_telemetry\n",
        "\n",
        "send_example_telemetry(\"time_series_transformers_notebook\", framework=\"pytorch\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4m_7_MKqmzfE",
      "metadata": {
        "id": "4m_7_MKqmzfE"
      },
      "source": [
        "## Load Dataset\n",
        "\n",
        "In this blog post, we'll use the `tourism_monthly` dataset, which is available on the [Hugging Face Hub](https://huggingface.co/datasets/monash_tsf). This dataset contains monthly tourism volumes for 366 regions in Australia.\n",
        "\n",
        "This dataset is part of the [Monash Time Series Forecasting](https://forecastingdata.org/) repository, a collection of  time series datasets from a number of domains. It can be viewed as the GLUE benchmark of time series forecasting."
      ]
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              "Downloading builder script:   0%|          | 0.00/24.9k [00:00<?, ?B/s]"
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          "data": {
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              "Downloading extra modules:   0%|          | 0.00/7.54k [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset monash_tsf/tourism_monthly to /root/.cache/huggingface/datasets/monash_tsf/tourism_monthly/1.0.0/82998723d55d6edbc664cfaebe371004d849846ee5f61b57f1f974000d44c050...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "46dc00c2a59d4211bb90c5d4d4031b94"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/366 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3afa8b226ec14535ae8eafc00789222f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/366 [00:00<?, ? examples/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/366 [00:00<?, ? examples/s]"
            ],
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset monash_tsf downloaded and prepared to /root/.cache/huggingface/datasets/monash_tsf/tourism_monthly/1.0.0/82998723d55d6edbc664cfaebe371004d849846ee5f61b57f1f974000d44c050. Subsequent calls will reuse this data.\n"
          ]
        },
        {
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              "model_id": "6285591079e2432b8c88c9151bd8ff36"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"monash_tsf\", \"tourism_monthly\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d45ae147",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d45ae147",
        "outputId": "a1cd85f0-129a-42aa-a831-f2cf707bcb3d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],\n",
              "        num_rows: 366\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],\n",
              "        num_rows: 366\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],\n",
              "        num_rows: 366\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "oHlRCUPkoN1N",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHlRCUPkoN1N",
        "outputId": "63db2005-4af0-40d5-c1e4-ba1c4b3251cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "train_example = dataset[\"train\"][0]\n",
        "train_example.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "gbVF5vAcqzJG",
      "metadata": {
        "id": "gbVF5vAcqzJG"
      },
      "source": [
        "The `start` simply indicates the start of the time series (as a datetime), and the `target` contains the actual values of the time series.\n",
        "\n",
        "The `start` will be useful to add time related features to the time series values, as extra input to the model (such as \"month of year\"). Since we know the frequency of the data is `monthly`, we know for instance that the second value has the timestamp `1979-02-01`, etc."
      ]
    },
    {
      "cell_type": "code",
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      "id": "1PDt8bvwoUbN",
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        "outputId": "0ad207fa-bf78-4662-c06a-07257286357d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1979-01-01 00:00:00\n",
            "[1149.8699951171875, 1053.8001708984375, 1388.8797607421875, 1783.3702392578125, 1921.025146484375, 2704.94482421875, 4184.41357421875, 4148.35400390625, 2620.72509765625, 1650.300048828125, 1115.9200439453125, 1370.6251220703125, 1096.31494140625, 978.4600219726562, 1294.68505859375, 1480.465087890625, 1748.865234375, 2216.920166015625, 4690.5185546875, 4682.8642578125, 2459.579833984375, 1484.4901123046875, 1028.985107421875, 1109.3648681640625, 960.8751220703125, 896.35009765625, 1118.6551513671875, 1619.9949951171875, 1847.994873046875, 2367.044921875, 4991.16015625, 4772.9443359375, 2894.678466796875, 1860.4801025390625, 1185.150146484375, 1313.659912109375, 1160.9150390625, 1061.5048828125, 1301.77001953125, 1794.3797607421875, 2106.455078125, 2789.034912109375, 4917.8466796875, 4994.4833984375, 3016.754150390625, 1941.505126953125, 1234.135009765625, 1378.72021484375, 1182.9749755859375, 1081.6600341796875, 1424.110107421875, 1774.5350341796875, 2115.420166015625, 2804.840087890625, 4849.498046875, 4937.47509765625, 3074.2236328125, 2063.42529296875, 1297.355224609375, 1350.710205078125, 1224.360107421875, 1165.815185546875, 1409.3299560546875, 2116.5498046875, 2357.135009765625, 2995.0703125, 5295.2119140625, 4957.90478515625, 3321.959228515625, 2221.18017578125, 1345.9000244140625, 1514.01513671875, 1239.5501708984375, 1172.159912109375, 1518.9752197265625, 1996.8751220703125, 2248.68505859375, 3053.440185546875, 5019.45361328125, 5466.7802734375, 3235.167724609375, 2157.97998046875, 1379.7252197265625, 1728.0400390625, 1350.10986328125, 1216.014892578125, 1751.3251953125, 1805.320068359375, 2570.02490234375, 3204.240234375, 5395.72021484375, 6078.82861328125, 3587.098388671875, 2285.195068359375, 1582.18994140625, 1787.4298095703125, 1554.8701171875, 1409.8648681640625, 1612.125, 2286.239990234375, 2913.755126953125, 3645.908447265625, 5956.70849609375, 6326.97509765625, 3914.66015625, 2617.675048828125, 1675.1650390625, 2139.219970703125, 1715.4898681640625, 1663.5799560546875, 2053.699951171875, 2354.929931640625, 3038.591796875, 3470.609375, 6606.18359375, 6587.63671875, 4133.78271484375, 2960.0244140625, 1762.5849609375, 2125.64013671875, 1815.9150390625, 1632.31494140625, 2210.39501953125, 2210.215087890625, 3099.269287109375, 3468.77783203125, 6482.92529296875, 6665.48486328125, 4006.36181640625, 2882.3349609375, 1775.2498779296875, 2171.64990234375, 1796.4749755859375, 1692.349853515625, 1949.78515625, 2680.630126953125, 2645.949951171875, 3414.742919921875, 5772.876953125]\n"
          ]
        }
      ],
      "source": [
        "print(train_example[\"start\"])\n",
        "print(train_example[\"target\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "DfkPxhCkquKL",
      "metadata": {
        "id": "DfkPxhCkquKL"
      },
      "source": [
        "The validation set contains the same data as the training set, just for a `prediction_length` longer amount of time. This allows us to validate the model's predictions against the ground truth.\n",
        "\n",
        "The test set is again one `prediction_length` longer data compared to the validation set (or some multiple of  `prediction_length` longer data compared to the training set for testing on multiple rolling windows)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eRQhm4EGpa0y",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRQhm4EGpa0y",
        "outputId": "6c653068-ba9a-4692-b1cd-5cacd916c600"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "validation_example = dataset[\"validation\"][0]\n",
        "validation_example.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "x5PI_Jn7rDfj",
      "metadata": {
        "id": "x5PI_Jn7rDfj"
      },
      "source": [
        "The initial values are exactly the same as the corresponding training example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "yHQnXZbUpg8q",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHQnXZbUpg8q",
        "outputId": "3126d770-0d43-47b2-d6a3-3de2835fe700"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1979-01-01 00:00:00\n",
            "[1149.8699951171875, 1053.8001708984375, 1388.8797607421875, 1783.3702392578125, 1921.025146484375, 2704.94482421875, 4184.41357421875, 4148.35400390625, 2620.72509765625, 1650.300048828125, 1115.9200439453125, 1370.6251220703125, 1096.31494140625, 978.4600219726562, 1294.68505859375, 1480.465087890625, 1748.865234375, 2216.920166015625, 4690.5185546875, 4682.8642578125, 2459.579833984375, 1484.4901123046875, 1028.985107421875, 1109.3648681640625, 960.8751220703125, 896.35009765625, 1118.6551513671875, 1619.9949951171875, 1847.994873046875, 2367.044921875, 4991.16015625, 4772.9443359375, 2894.678466796875, 1860.4801025390625, 1185.150146484375, 1313.659912109375, 1160.9150390625, 1061.5048828125, 1301.77001953125, 1794.3797607421875, 2106.455078125, 2789.034912109375, 4917.8466796875, 4994.4833984375, 3016.754150390625, 1941.505126953125, 1234.135009765625, 1378.72021484375, 1182.9749755859375, 1081.6600341796875, 1424.110107421875, 1774.5350341796875, 2115.420166015625, 2804.840087890625, 4849.498046875, 4937.47509765625, 3074.2236328125, 2063.42529296875, 1297.355224609375, 1350.710205078125, 1224.360107421875, 1165.815185546875, 1409.3299560546875, 2116.5498046875, 2357.135009765625, 2995.0703125, 5295.2119140625, 4957.90478515625, 3321.959228515625, 2221.18017578125, 1345.9000244140625, 1514.01513671875, 1239.5501708984375, 1172.159912109375, 1518.9752197265625, 1996.8751220703125, 2248.68505859375, 3053.440185546875, 5019.45361328125, 5466.7802734375, 3235.167724609375, 2157.97998046875, 1379.7252197265625, 1728.0400390625, 1350.10986328125, 1216.014892578125, 1751.3251953125, 1805.320068359375, 2570.02490234375, 3204.240234375, 5395.72021484375, 6078.82861328125, 3587.098388671875, 2285.195068359375, 1582.18994140625, 1787.4298095703125, 1554.8701171875, 1409.8648681640625, 1612.125, 2286.239990234375, 2913.755126953125, 3645.908447265625, 5956.70849609375, 6326.97509765625, 3914.66015625, 2617.675048828125, 1675.1650390625, 2139.219970703125, 1715.4898681640625, 1663.5799560546875, 2053.699951171875, 2354.929931640625, 3038.591796875, 3470.609375, 6606.18359375, 6587.63671875, 4133.78271484375, 2960.0244140625, 1762.5849609375, 2125.64013671875, 1815.9150390625, 1632.31494140625, 2210.39501953125, 2210.215087890625, 3099.269287109375, 3468.77783203125, 6482.92529296875, 6665.48486328125, 4006.36181640625, 2882.3349609375, 1775.2498779296875, 2171.64990234375, 1796.4749755859375, 1692.349853515625, 1949.78515625, 2680.630126953125, 2645.949951171875, 3414.742919921875, 5772.876953125, 6053.7041015625, 3878.12841796875, 2806.514892578125, 1735.5382080078125, 2128.919921875, 1608.01416015625, 1441.330078125, 2068.235107421875, 2207.610107421875, 2918.409912109375, 3400.81787109375, 6048.7421875, 6483.14013671875, 4063.502685546875, 2900.22998046875, 1907.094970703125, 2338.510009765625, 1787.1650390625, 1699.6451416015625, 1979.105224609375, 2824.260009765625, 3076.5048828125, 3402.5849609375, 5985.830078125]\n"
          ]
        }
      ],
      "source": [
        "print(validation_example[\"start\"])\n",
        "print(validation_example[\"target\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "vQU8V7hPrIZA",
      "metadata": {
        "id": "vQU8V7hPrIZA"
      },
      "source": [
        "However, this example has `prediction_length=24` additional values compared to the training example. Let us verify it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "__j4Z5Ohp8gg",
      "metadata": {
        "id": "__j4Z5Ohp8gg"
      },
      "outputs": [],
      "source": [
        "freq = \"1M\"\n",
        "prediction_length = 24\n",
        "\n",
        "assert len(train_example[\"target\"]) + prediction_length == len(\n",
        "    validation_example[\"target\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "PGq2e9D-rhtg",
      "metadata": {
        "id": "PGq2e9D-rhtg"
      },
      "source": [
        "Let's visualize this:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cYDyml0tsnlL",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "cYDyml0tsnlL",
        "outputId": "099de09a-a882-4bc8-9cce-4a9e7e58aef9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "figure, axes = plt.subplots()\n",
        "axes.plot(train_example[\"target\"], color=\"blue\")\n",
        "axes.plot(validation_example[\"target\"], color=\"red\", alpha=0.5)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4pGV6_ZduUaA",
      "metadata": {
        "id": "4pGV6_ZduUaA"
      },
      "source": [
        "Let's split up the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7eb15a6a",
      "metadata": {
        "id": "7eb15a6a"
      },
      "outputs": [],
      "source": [
        "train_dataset = dataset[\"train\"]\n",
        "test_dataset = dataset[\"test\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0125722c",
      "metadata": {
        "id": "0125722c"
      },
      "source": [
        "## Update `start` to `pd.Period`\n",
        "\n",
        "The first thing we'll do is convert the `start` feature of each time series to a pandas `Period` index using the data's `freq`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "249a9da4",
      "metadata": {
        "id": "249a9da4"
      },
      "outputs": [],
      "source": [
        "from functools import lru_cache\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "@lru_cache(10_000)\n",
        "def convert_to_pandas_period(date, freq):\n",
        "    return pd.Period(date, freq)\n",
        "\n",
        "\n",
        "def transform_start_field(batch, freq):\n",
        "    batch[\"start\"] = [convert_to_pandas_period(date, freq) for date in batch[\"start\"]]\n",
        "    return batch"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "D7goNkUB1MPB",
      "metadata": {
        "id": "D7goNkUB1MPB"
      },
      "source": [
        "We now use `datasets`' [`set_transform`](https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform) functionality to do this on-the-fly in place:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b43c7551",
      "metadata": {
        "id": "b43c7551"
      },
      "outputs": [],
      "source": [
        "from functools import partial\n",
        "\n",
        "train_dataset.set_transform(partial(transform_start_field, freq=freq))\n",
        "test_dataset.set_transform(partial(transform_start_field, freq=freq))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "50efb119",
      "metadata": {
        "id": "50efb119"
      },
      "source": [
        "## Define the model\n",
        "\n",
        "Next, let's instantiate a model. The model will be trained from scratch, hence we won't use the `from_pretrained` method here, but rather randomly initialize the model from a [`config`](https://huggingface.co/docs/transformers/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig).\n",
        "\n",
        "We specify a couple of additional parameters to the model:\n",
        "- `prediction_length` (in our case, `24` months): this is the horizon that the decoder of the Transformer will learn to predict for;\n",
        "- `context_length`: the model will set the `context_length` (input of the encoder) equal to the `prediction_length`, if no `context_length` is specified;\n",
        "- `lags` for a given frequency: these specify how much we \"look back\", to be added as additional features. e.g. for a `Daily` frequency we might consider a look back of `[1, 2, 7, 30, ...]` or in other words look back 1, 2, ... days while for `Minute` data we might consider `[1, 30, 60, 60*24, ...]` etc.;\n",
        "- the number of time features: in our case, this will be `2` as we'll add `MonthOfYear` and `Age` features;\n",
        "- the number of static categorical features: in our case, this will be just `1` as we'll add a single \"time series ID\" feature;\n",
        "- the cardinality: the number of values of each static categorical feature, as a list which for our case will be `[366]` as we have 366 different time series\n",
        "- the embedding dimension: the embedding dimension for each static categorical feature, as a list, for example `[3]` meaning the model will learn an embedding vector of size `3` for each of the `366` time series (regions).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0z4YFov8CNUu",
      "metadata": {
        "id": "0z4YFov8CNUu"
      },
      "source": [
        "Let's use the default lags provided by GluonTS for the given frequency (\"monthly\"):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6CF4M8Ms7W-q",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CF4M8Ms7W-q",
        "outputId": "27db2468-7897-4264-a5ab-4bb7f3382a39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 23, 24, 25, 35, 36, 37]\n"
          ]
        }
      ],
      "source": [
        "from gluonts.time_feature import get_lags_for_frequency\n",
        "\n",
        "lags_sequence = get_lags_for_frequency(freq)\n",
        "print(lags_sequence)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "q6ZuFx8yCSAM",
      "metadata": {
        "id": "q6ZuFx8yCSAM"
      },
      "source": [
        "This means that we'll look back up to 37 months for each time step, as additional features.\n",
        "\n",
        "Let's also check the default time features which GluonTS provides us:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "VlP_0E5I76lg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlP_0E5I76lg",
        "outputId": "71cf64d8-b253-480d-e14e-15c3edc86470"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<function month_of_year at 0x7ff9cc3afaf0>]\n"
          ]
        }
      ],
      "source": [
        "from gluonts.time_feature import time_features_from_frequency_str\n",
        "\n",
        "time_features = time_features_from_frequency_str(freq)\n",
        "print(time_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "m0_f7lm9CbNG",
      "metadata": {
        "id": "m0_f7lm9CbNG"
      },
      "source": [
        "In this case, there's only a single feature, namely \"month of year\". This means that for each time step, we'll add the month as a scalar value (e.g. `1` in case the timestamp is \"january\", `2` in case the timestamp is \"february\", etc.).\n",
        "\n",
        "We now have everything to define the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3dda0e78",
      "metadata": {
        "id": "3dda0e78"
      },
      "outputs": [],
      "source": [
        "from transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerForPrediction\n",
        "\n",
        "config = TimeSeriesTransformerConfig(\n",
        "    prediction_length=prediction_length,\n",
        "    # context length:\n",
        "    context_length=prediction_length * 2,\n",
        "    # lags coming from helper given the freq:\n",
        "    lags_sequence=lags_sequence,\n",
        "    # we'll add 2 time features (\"month of year\" and \"age\", see further):\n",
        "    num_time_features=len(time_features) + 1,\n",
        "    # we have a single static categorical feature, namely time series ID:\n",
        "    num_static_categorical_features=1,\n",
        "    # it has 366 possible values:\n",
        "    cardinality=[len(train_dataset)],\n",
        "    # the model will learn an embedding of size 2 for each of the 366 possible values:\n",
        "    embedding_dimension=[2],\n",
        "    \n",
        "    # transformer params:\n",
        "    encoder_layers=4,\n",
        "    decoder_layers=4,\n",
        "    d_model=32,\n",
        ")\n",
        "\n",
        "model = TimeSeriesTransformerForPrediction(config)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "T6rCeV4dsUnF",
      "metadata": {
        "id": "T6rCeV4dsUnF"
      },
      "source": [
        "Note that, similar to other models in the 🤗 Transformers library, [`TimeSeriesTransformerModel`](https://huggingface.co/docs/transformers/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel) corresponds to the encoder-decoder Transformer without any head on top, and [`TimeSeriesTransformerForPrediction`](https://huggingface.co/docs/transformers/model_doc/time_series_transformer#transformers.TimeSeriesTransformerForPrediction) corresponds to `TimeSeriesTransformerModel` with a **distribution head** on top. By default, the model uses a Student-t distribution (but this is configurable):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "EaoKZyujsuIA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EaoKZyujsuIA",
        "outputId": "074493a6-ab37-448d-83d5-90e1e210e09d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'student_t'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "model.config.distribution_output"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "feQYVhgus-yl",
      "metadata": {
        "id": "feQYVhgus-yl"
      },
      "source": [
        "This is an important difference with Transformers for NLP, where the head typically consists of a fixed categorical distribution implemented as an `nn.Linear` layer."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "af82c60d",
      "metadata": {
        "id": "af82c60d"
      },
      "source": [
        "## Define Transformations\n",
        "\n",
        "Next, we define the transformations for the data, in particular for the creation of the time features (based on the dataset or universal ones).\n",
        "\n",
        "Again, we'll use the GluonTS library for this. We define a `Chain` of transformations (which is a bit comparable to `torchvision.transforms.Compose` for images). It allows us to combine several transformations into a single pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "tR87yKPSn8SZ",
      "metadata": {
        "id": "tR87yKPSn8SZ"
      },
      "outputs": [],
      "source": [
        "from gluonts.time_feature import (\n",
        "    time_features_from_frequency_str,\n",
        "    TimeFeature,\n",
        "    get_lags_for_frequency,\n",
        ")\n",
        "from gluonts.dataset.field_names import FieldName\n",
        "from gluonts.transform import (\n",
        "    AddAgeFeature,\n",
        "    AddObservedValuesIndicator,\n",
        "    AddTimeFeatures,\n",
        "    AsNumpyArray,\n",
        "    Chain,\n",
        "    ExpectedNumInstanceSampler,\n",
        "    InstanceSplitter,\n",
        "    RemoveFields,\n",
        "    SelectFields,\n",
        "    SetField,\n",
        "    TestSplitSampler,\n",
        "    Transformation,\n",
        "    ValidationSplitSampler,\n",
        "    VstackFeatures,\n",
        "    RenameFields,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4Qql4CInFWO7",
      "metadata": {
        "id": "4Qql4CInFWO7"
      },
      "source": [
        "The transformations below are annotated with comments, to explain what they do. At a high level, we will iterate over the individual time series of our dataset and add/remove fields or features:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20fe036e",
      "metadata": {
        "id": "20fe036e"
      },
      "outputs": [],
      "source": [
        "from transformers import PretrainedConfig\n",
        "\n",
        "\n",
        "def create_transformation(freq: str, config: PretrainedConfig) -> Transformation:\n",
        "    remove_field_names = []\n",
        "    if config.num_static_real_features == 0:\n",
        "        remove_field_names.append(FieldName.FEAT_STATIC_REAL)\n",
        "    if config.num_dynamic_real_features == 0:\n",
        "        remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)\n",
        "    if config.num_static_categorical_features == 0:\n",
        "        remove_field_names.append(FieldName.FEAT_STATIC_CAT)\n",
        "\n",
        "    # a bit like torchvision.transforms.Compose\n",
        "    return Chain(\n",
        "        # step 1: remove static/dynamic fields if not specified\n",
        "        [RemoveFields(field_names=remove_field_names)]\n",
        "        # step 2: convert the data to NumPy (potentially not needed)\n",
        "        + (\n",
        "            [\n",
        "                AsNumpyArray(\n",
        "                    field=FieldName.FEAT_STATIC_CAT,\n",
        "                    expected_ndim=1,\n",
        "                    dtype=int,\n",
        "                )\n",
        "            ]\n",
        "            if config.num_static_categorical_features > 0\n",
        "            else []\n",
        "        )\n",
        "        + (\n",
        "            [\n",
        "                AsNumpyArray(\n",
        "                    field=FieldName.FEAT_STATIC_REAL,\n",
        "                    expected_ndim=1,\n",
        "                )\n",
        "            ]\n",
        "            if config.num_static_real_features > 0\n",
        "            else []\n",
        "        )\n",
        "        + [\n",
        "            AsNumpyArray(\n",
        "                field=FieldName.TARGET,\n",
        "                # we expect an extra dim for the multivariate case:\n",
        "                expected_ndim=1 if config.input_size == 1 else 2,\n",
        "            ),\n",
        "            # step 3: handle the NaN's by filling in the target with zero\n",
        "            # and return the mask (which is in the observed values)\n",
        "            # true for observed values, false for nan's\n",
        "            # the decoder uses this mask (no loss is incurred for unobserved values)\n",
        "            # see loss_weights inside the xxxForPrediction model\n",
        "            AddObservedValuesIndicator(\n",
        "                target_field=FieldName.TARGET,\n",
        "                output_field=FieldName.OBSERVED_VALUES,\n",
        "            ),\n",
        "            # step 4: add temporal features based on freq of the dataset\n",
        "            # month of year in the case when freq=\"M\"\n",
        "            # these serve as positional encodings\n",
        "            AddTimeFeatures(\n",
        "                start_field=FieldName.START,\n",
        "                target_field=FieldName.TARGET,\n",
        "                output_field=FieldName.FEAT_TIME,\n",
        "                time_features=time_features_from_frequency_str(freq),\n",
        "                pred_length=config.prediction_length,\n",
        "            ),\n",
        "            # step 5: add another temporal feature (just a single number)\n",
        "            # tells the model where in the life the value of the time series is\n",
        "            # sort of running counter\n",
        "            AddAgeFeature(\n",
        "                target_field=FieldName.TARGET,\n",
        "                output_field=FieldName.FEAT_AGE,\n",
        "                pred_length=config.prediction_length,\n",
        "                log_scale=True,\n",
        "            ),\n",
        "            # step 6: vertically stack all the temporal features into the key FEAT_TIME\n",
        "            VstackFeatures(\n",
        "                output_field=FieldName.FEAT_TIME,\n",
        "                input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]\n",
        "                + (\n",
        "                    [FieldName.FEAT_DYNAMIC_REAL]\n",
        "                    if config.num_dynamic_real_features > 0\n",
        "                    else []\n",
        "                ),\n",
        "            ),\n",
        "            # step 7: rename to match HuggingFace names\n",
        "            RenameFields(\n",
        "                mapping={\n",
        "                    FieldName.FEAT_STATIC_CAT: \"static_categorical_features\",\n",
        "                    FieldName.FEAT_STATIC_REAL: \"static_real_features\",\n",
        "                    FieldName.FEAT_TIME: \"time_features\",\n",
        "                    FieldName.TARGET: \"values\",\n",
        "                    FieldName.OBSERVED_VALUES: \"observed_mask\",\n",
        "                }\n",
        "            ),\n",
        "        ]\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8bab46d0",
      "metadata": {
        "id": "8bab46d0"
      },
      "source": [
        "## Define `InstanceSplitter`\n",
        "\n",
        "For training/validation/testing we next create an `InstanceSplitter` which is used to sample windows from the dataset (as, remember, we can't pass the entire history of values to the Transformer due to time- and memory constraints).\n",
        "\n",
        "The instance splitter samples random `context_length` sized and subsequent `prediction_length` sized windows from the data, and appends a `past_` or `future_` key to any temporal keys for the respective windows. This makes sure that the `values` will be split into `past_values` and subsequent `future_values` keys, which will serve as the encoder and decoder inputs respectively. The same happens for any keys in the `time_series_fields` argument:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cae7600d",
      "metadata": {
        "id": "cae7600d"
      },
      "outputs": [],
      "source": [
        "from gluonts.transform.sampler import InstanceSampler\n",
        "from typing import Optional\n",
        "\n",
        "\n",
        "def create_instance_splitter(\n",
        "    config: PretrainedConfig,\n",
        "    mode: str,\n",
        "    train_sampler: Optional[InstanceSampler] = None,\n",
        "    validation_sampler: Optional[InstanceSampler] = None,\n",
        ") -> Transformation:\n",
        "    assert mode in [\"train\", \"validation\", \"test\"]\n",
        "\n",
        "    instance_sampler = {\n",
        "        \"train\": train_sampler\n",
        "        or ExpectedNumInstanceSampler(\n",
        "            num_instances=1.0, min_future=config.prediction_length\n",
        "        ),\n",
        "        \"validation\": validation_sampler\n",
        "        or ValidationSplitSampler(min_future=config.prediction_length),\n",
        "        \"test\": TestSplitSampler(),\n",
        "    }[mode]\n",
        "\n",
        "    return InstanceSplitter(\n",
        "        target_field=\"values\",\n",
        "        is_pad_field=FieldName.IS_PAD,\n",
        "        start_field=FieldName.START,\n",
        "        forecast_start_field=FieldName.FORECAST_START,\n",
        "        instance_sampler=instance_sampler,\n",
        "        past_length=config.context_length + max(config.lags_sequence),\n",
        "        future_length=config.prediction_length,\n",
        "        time_series_fields=[\"time_features\", \"observed_mask\"],\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "958e050d",
      "metadata": {
        "id": "958e050d"
      },
      "source": [
        "## Create PyTorch DataLoaders\n",
        "\n",
        "Next, it's time to create PyTorch DataLoaders, which allow us to have batches of (input, output pairs) - or in other words (`past_values`, `future_values`)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch_lightning"
      ],
      "metadata": {
        "id": "99UVRL1NbJhe",
        "outputId": "05cd25f6-ae3a-41eb-9b7c-1f8c9bcfa369",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "99UVRL1NbJhe",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: lightning-utilities, torchmetrics, pytorch_lightning\n",
            "Successfully installed lightning-utilities-0.8.0 pytorch_lightning-2.0.1.post0 torchmetrics-0.11.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6995101c",
      "metadata": {
        "id": "6995101c"
      },
      "outputs": [],
      "source": [
        "from gluonts.itertools import Cyclic, IterableSlice, PseudoShuffled\n",
        "from gluonts.torch.util import IterableDataset\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from typing import Iterable\n",
        "\n",
        "\n",
        "def create_train_dataloader(\n",
        "    config: PretrainedConfig,\n",
        "    freq,\n",
        "    data,\n",
        "    batch_size: int,\n",
        "    num_batches_per_epoch: int,\n",
        "    shuffle_buffer_length: Optional[int] = None,\n",
        "    **kwargs,\n",
        ") -> Iterable:\n",
        "    PREDICTION_INPUT_NAMES = [\n",
        "        \"past_time_features\",\n",
        "        \"past_values\",\n",
        "        \"past_observed_mask\",\n",
        "        \"future_time_features\",\n",
        "    ]\n",
        "    if config.num_static_categorical_features > 0:\n",
        "        PREDICTION_INPUT_NAMES.append(\"static_categorical_features\")\n",
        "\n",
        "    if config.num_static_real_features > 0:\n",
        "        PREDICTION_INPUT_NAMES.append(\"static_real_features\")\n",
        "\n",
        "    TRAINING_INPUT_NAMES = PREDICTION_INPUT_NAMES + [\n",
        "        \"future_values\",\n",
        "        \"future_observed_mask\",\n",
        "    ]\n",
        "\n",
        "    transformation = create_transformation(freq, config)\n",
        "    transformed_data = transformation.apply(data, is_train=True)\n",
        "\n",
        "    # we initialize a Training instance\n",
        "    instance_splitter = create_instance_splitter(config, \"train\") + SelectFields(\n",
        "        TRAINING_INPUT_NAMES\n",
        "    )\n",
        "\n",
        "    # the instance splitter will sample a window of\n",
        "    # context length + lags + prediction length (from the 366 possible transformed time series)\n",
        "    # randomly from within the target time series and return an iterator.\n",
        "    training_instances = instance_splitter.apply(\n",
        "        Cyclic(transformed_data)\n",
        "        if shuffle_buffer_length is None\n",
        "        else PseudoShuffled(\n",
        "            Cyclic(transformed_data),\n",
        "            shuffle_buffer_length=shuffle_buffer_length,\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # from the training instances iterator we now return a Dataloader which will\n",
        "    # continue to sample random windows for as long as it is called\n",
        "    # to return batch_size of the appropriate tensors ready for training!\n",
        "    return IterableSlice(\n",
        "        iter(\n",
        "            DataLoader(\n",
        "                IterableDataset(training_instances),\n",
        "                batch_size=batch_size,\n",
        "                **kwargs,\n",
        "            )\n",
        "        ),\n",
        "        num_batches_per_epoch,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "10c55455",
      "metadata": {
        "id": "10c55455"
      },
      "outputs": [],
      "source": [
        "def create_test_dataloader(\n",
        "    config: PretrainedConfig,\n",
        "    freq,\n",
        "    data,\n",
        "    batch_size: int,\n",
        "    **kwargs,\n",
        "):\n",
        "    PREDICTION_INPUT_NAMES = [\n",
        "        \"past_time_features\",\n",
        "        \"past_values\",\n",
        "        \"past_observed_mask\",\n",
        "        \"future_time_features\",\n",
        "    ]\n",
        "    if config.num_static_categorical_features > 0:\n",
        "        PREDICTION_INPUT_NAMES.append(\"static_categorical_features\")\n",
        "\n",
        "    if config.num_static_real_features > 0:\n",
        "        PREDICTION_INPUT_NAMES.append(\"static_real_features\")\n",
        "\n",
        "    transformation = create_transformation(freq, config)\n",
        "    transformed_data = transformation.apply(data, is_train=False)\n",
        "\n",
        "    # we create a Test Instance splitter which will sample the very last\n",
        "    # context window seen during training only for the encoder.\n",
        "    instance_sampler = create_instance_splitter(config, \"test\") + SelectFields(\n",
        "        PREDICTION_INPUT_NAMES\n",
        "    )\n",
        "\n",
        "    # we apply the transformations in test mode\n",
        "    testing_instances = instance_sampler.apply(transformed_data, is_train=False)\n",
        "\n",
        "    # This returns a Dataloader which will go over the dataset once.\n",
        "    return DataLoader(\n",
        "        IterableDataset(testing_instances), batch_size=batch_size, **kwargs\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20e2338b",
      "metadata": {
        "id": "20e2338b"
      },
      "outputs": [],
      "source": [
        "train_dataloader = create_train_dataloader(\n",
        "    config=config,\n",
        "    freq=freq,\n",
        "    data=train_dataset,\n",
        "    batch_size=256,\n",
        "    num_batches_per_epoch=100,\n",
        ")\n",
        "\n",
        "test_dataloader = create_test_dataloader(\n",
        "    config=config,\n",
        "    freq=freq,\n",
        "    data=test_dataset,\n",
        "    batch_size=64,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ES2U8g-2G2Jd",
      "metadata": {
        "id": "ES2U8g-2G2Jd"
      },
      "source": [
        "Let's check the first batch:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "YU2h9OOB5IsX",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YU2h9OOB5IsX",
        "outputId": "71baec84-d1aa-421e-b024-00b0a4e1e98b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "past_time_features torch.Size([256, 85, 2]) torch.FloatTensor\n",
            "past_values torch.Size([256, 85]) torch.FloatTensor\n",
            "past_observed_mask torch.Size([256, 85]) torch.FloatTensor\n",
            "future_time_features torch.Size([256, 24, 2]) torch.FloatTensor\n",
            "static_categorical_features torch.Size([256, 1]) torch.LongTensor\n",
            "future_values torch.Size([256, 24]) torch.FloatTensor\n",
            "future_observed_mask torch.Size([256, 24]) torch.FloatTensor\n"
          ]
        }
      ],
      "source": [
        "batch = next(iter(train_dataloader))\n",
        "for k, v in batch.items():\n",
        "    print(k, v.shape, v.type())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "HvvPlSF8HBYd",
      "metadata": {
        "id": "HvvPlSF8HBYd"
      },
      "source": [
        "As can be seen, we don't feed `input_ids` and `attention_mask` to the encoder (as would be the case for NLP models), but rather `past_values`, along with `past_observed_mask`, `past_time_features`, and `static_categorical_features`.\n",
        "\n",
        "The decoder inputs consist of `future_values`, `future_observed_mask` and `future_time_features`. The `future_values` can be seen as the equivalent of `decoder_input_ids` in NLP.\n",
        "\n",
        "We refer to the [docs](https://huggingface.co/docs/transformers/model_doc/time_series_transformer#transformers.TimeSeriesTransformerForPrediction.forward.past_values) for a detailed explanation for each of them."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "_cev4ufVv1yf",
      "metadata": {
        "id": "_cev4ufVv1yf"
      },
      "source": [
        "## Forward pass\n",
        "\n",
        "Let's perform a single forward pass with the batch we just created:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "sD8fav6qTApR",
      "metadata": {
        "id": "sD8fav6qTApR"
      },
      "outputs": [],
      "source": [
        "# perform forward pass\n",
        "outputs = model(\n",
        "    past_values=batch[\"past_values\"],\n",
        "    past_time_features=batch[\"past_time_features\"],\n",
        "    past_observed_mask=batch[\"past_observed_mask\"],\n",
        "    static_categorical_features=batch[\"static_categorical_features\"]\n",
        "    if config.num_static_categorical_features > 0\n",
        "    else None,\n",
        "    static_real_features=batch[\"static_real_features\"]\n",
        "    if config.num_static_real_features > 0\n",
        "    else None,\n",
        "    future_values=batch[\"future_values\"],\n",
        "    future_time_features=batch[\"future_time_features\"],\n",
        "    future_observed_mask=batch[\"future_observed_mask\"],\n",
        "    output_hidden_states=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "j2KnnHTCX4RC",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2KnnHTCX4RC",
        "outputId": "dfbda41a-1e89-4da5-811b-0806d642aa9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 9.079747200012207\n"
          ]
        }
      ],
      "source": [
        "print(\"Loss:\", outputs.loss.item())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "V9K8s2j9y8x7",
      "metadata": {
        "id": "V9K8s2j9y8x7"
      },
      "source": [
        "Note that the model is returning a loss. This is possible as the decoder automatically shifts the `future_values` one position to the right in order to have the labels. This allows computing a loss between the predicted values and the labels.\n",
        "\n",
        "Also note that the decoder uses a causal mask to not look into the future as the values it needs to predict are in the `future_values` tensor."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SxHDCa7vwPBF",
      "metadata": {
        "id": "SxHDCa7vwPBF"
      },
      "source": [
        "## Train the Model\n",
        "\n",
        "It's time to train the model! We'll use a standard PyTorch training loop.\n",
        "\n",
        "We will use the 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) library here, which automatically places the model, optimizer and dataloader on the appropriate `device`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "gMLYvQaNHuXQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMLYvQaNHuXQ",
        "outputId": "08945b0a-710f-4997-a90d-6b3ef4002874",
        "scrolled": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9.013245582580566\n",
            "7.403395175933838\n",
            "7.6720452308654785\n",
            "7.465734958648682\n",
            "7.118642330169678\n",
            "7.223761081695557\n",
            "7.457561492919922\n",
            "7.1515655517578125\n",
            "7.690324306488037\n",
            "7.630891799926758\n",
            "7.223060131072998\n",
            "7.427400588989258\n",
            "6.816844940185547\n",
            "7.623453617095947\n",
            "7.103296756744385\n",
            "6.752160549163818\n",
            "6.731922149658203\n",
            "7.306280612945557\n",
            "7.262670993804932\n",
            "7.37090539932251\n",
            "6.926530361175537\n",
            "7.2079901695251465\n",
            "7.453780651092529\n",
            "6.652652740478516\n",
            "7.375556945800781\n",
            "7.011942386627197\n",
            "7.236439228057861\n",
            "7.369480133056641\n",
            "7.066883563995361\n",
            "6.817388534545898\n",
            "7.625083923339844\n",
            "6.692563533782959\n",
            "7.2104620933532715\n",
            "7.181840419769287\n",
            "7.040696620941162\n",
            "6.748927593231201\n",
            "7.003507137298584\n",
            "7.187035083770752\n",
            "6.861521244049072\n",
            "6.8522419929504395\n"
          ]
        }
      ],
      "source": [
        "from accelerate import Accelerator\n",
        "from torch.optim import AdamW\n",
        "\n",
        "accelerator = Accelerator()\n",
        "device = accelerator.device\n",
        "\n",
        "model.to(device)\n",
        "optimizer = AdamW(model.parameters(), lr=6e-4, betas=(0.9, 0.95), weight_decay=1e-1)\n",
        "\n",
        "model, optimizer, train_dataloader = accelerator.prepare(\n",
        "    model,\n",
        "    optimizer,\n",
        "    train_dataloader,\n",
        ")\n",
        "\n",
        "model.train()\n",
        "for epoch in range(40):\n",
        "    for idx, batch in enumerate(train_dataloader):\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(\n",
        "            static_categorical_features=batch[\"static_categorical_features\"].to(device)\n",
        "            if config.num_static_categorical_features > 0\n",
        "            else None,\n",
        "            static_real_features=batch[\"static_real_features\"].to(device)\n",
        "            if config.num_static_real_features > 0\n",
        "            else None,\n",
        "            past_time_features=batch[\"past_time_features\"].to(device),\n",
        "            past_values=batch[\"past_values\"].to(device),\n",
        "            future_time_features=batch[\"future_time_features\"].to(device),\n",
        "            future_values=batch[\"future_values\"].to(device),\n",
        "            past_observed_mask=batch[\"past_observed_mask\"].to(device),\n",
        "            future_observed_mask=batch[\"future_observed_mask\"].to(device),\n",
        "        )\n",
        "        loss = outputs.loss\n",
        "\n",
        "        # Backpropagation\n",
        "        accelerator.backward(loss)\n",
        "        optimizer.step()\n",
        "\n",
        "        if idx % 100 == 0:\n",
        "            print(loss.item())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Q1U6YhaFXlSp",
      "metadata": {
        "id": "Q1U6YhaFXlSp"
      },
      "source": [
        "## Inference\n",
        "\n",
        "At inference time, it's recommended to use the `generate()` method for autoregressive generation, similar to NLP models.\n",
        "\n",
        "Forecasting involves getting data from the test instance sampler, which will sample the very last `context_length` sized window of values from each time series in the dataset, and pass it to the model. Note that we pass `future_time_features`, which are known ahead of time, to the decoder.\n",
        "\n",
        "The model will autoregressively sample a certain number of values from the predicted distribution and pass them back to the decoder to return the prediction outputs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7c7482c1",
      "metadata": {
        "id": "7c7482c1"
      },
      "outputs": [],
      "source": [
        "model.eval()\n",
        "\n",
        "forecasts = []\n",
        "\n",
        "for batch in test_dataloader:\n",
        "    outputs = model.generate(\n",
        "        static_categorical_features=batch[\"static_categorical_features\"].to(device)\n",
        "        if config.num_static_categorical_features > 0\n",
        "        else None,\n",
        "        static_real_features=batch[\"static_real_features\"].to(device)\n",
        "        if config.num_static_real_features > 0\n",
        "        else None,\n",
        "        past_time_features=batch[\"past_time_features\"].to(device),\n",
        "        past_values=batch[\"past_values\"].to(device),\n",
        "        future_time_features=batch[\"future_time_features\"].to(device),\n",
        "        past_observed_mask=batch[\"past_observed_mask\"].to(device),\n",
        "    )\n",
        "    forecasts.append(outputs.sequences.cpu().numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "kPLiRcOeZR67",
      "metadata": {
        "id": "kPLiRcOeZR67"
      },
      "source": [
        "The model outputs a tensor of shape (`batch_size`, `number of samples`, `prediction length`). \n",
        "\n",
        "In this case, we get `100` possible values for the next `24` months (for each example in the batch which is of size `64`):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "DwAfSZitZNAQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwAfSZitZNAQ",
        "outputId": "114e0301-9ed9-4506-b4db-22705cdb81e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 100, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "forecasts[0].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fCTBw_t91xwH",
      "metadata": {
        "id": "fCTBw_t91xwH"
      },
      "source": [
        "We'll stack them vertically, to get forecasts for all time-series in the test dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "du1GyJVXlpHp",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du1GyJVXlpHp",
        "outputId": "62d7903e-2538-40ff-8e15-b0fd52966cb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(366, 100, 24)\n"
          ]
        }
      ],
      "source": [
        "forecasts = np.vstack(forecasts)\n",
        "print(forecasts.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wlvFCdgiA9oy",
      "metadata": {
        "id": "wlvFCdgiA9oy"
      },
      "source": [
        "We can evaluate the resulting forecast with respect to the ground truth out of sample values present in the test set. For that, we'll use the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library, which includes the [MASE](https://huggingface.co/spaces/evaluate-metric/mase) and [sMAPE](https://huggingface.co/spaces/evaluate-metric/smape) metrics.\n",
        "\n",
        "We calculate both metrics for each time series in the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0yb9RnczYE4z",
      "metadata": {
        "id": "0yb9RnczYE4z",
        "outputId": "e7be17b1-c5e8-40fd-e63d-8d5812598375",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "f6732d72c2ee4a018b99724b6e6db3ba",
            "9dcff1b4f1d043369fc88ea7682ca580",
            "4c8f4a86e9774005a65fe6bb705f2075",
            "96df6a2ce41f48f9821a47241ef84fb9",
            "224346993fa94b46869f1121d7176167",
            "7828c06634994f42b281ee52207ea520",
            "f161564ef1db4eb6b7ad63639b76cbf5",
            "3a09524085b04cf7b47b44efdbd5ebc8",
            "cdc2aa28874e45d284296dddd3b50127",
            "f103d5a0a1c648fb83eaee31aa313229",
            "99bb609bf77c41c997f0616970dd497e",
            "867935f175b5496bbf560bdd7a9bdf0f",
            "1a4dee3cf9914ce0baac521050fe8c7d",
            "9c854abc3dd84c81abedfac3e31ac695",
            "7de63837e0844710a3b1ae8aab6dfcf9",
            "387df121dfea4ca49f8c6ac5a1b99344",
            "fb35455e8cce4e8489b1986647a5242f",
            "48400b4719d9415693ab8c45da07bb1e",
            "5bf506ea48ec44b08c18e1eef4d3ecbd",
            "c1931861b7c84d59b93dbf1b67a327ce",
            "15a9cafef4ba43e2b4772885a7e5505d",
            "32f8a24b3c39477db6fb3ed0721f18b2"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.50k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6732d72c2ee4a018b99724b6e6db3ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "867935f175b5496bbf560bdd7a9bdf0f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from evaluate import load\n",
        "from gluonts.time_feature import get_seasonality\n",
        "\n",
        "mase_metric = load(\"evaluate-metric/mase\")\n",
        "smape_metric = load(\"evaluate-metric/smape\")\n",
        "\n",
        "forecast_median = np.median(forecasts, 1)\n",
        "\n",
        "mase_metrics = []\n",
        "smape_metrics = []\n",
        "for item_id, ts in enumerate(test_dataset):\n",
        "    training_data = ts[\"target\"][:-prediction_length]\n",
        "    ground_truth = ts[\"target\"][-prediction_length:]\n",
        "    mase = mase_metric.compute(\n",
        "        predictions=forecast_median[item_id],\n",
        "        references=np.array(ground_truth),\n",
        "        training=np.array(training_data),\n",
        "        periodicity=get_seasonality(freq),\n",
        "    )\n",
        "    mase_metrics.append(mase[\"mase\"])\n",
        "\n",
        "    smape = smape_metric.compute(\n",
        "        predictions=forecast_median[item_id],\n",
        "        references=np.array(ground_truth),\n",
        "    )\n",
        "    smape_metrics.append(smape[\"smape\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fuuFbNYdZlIR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuuFbNYdZlIR",
        "outputId": "dbe8f98d-177a-4e18-e754-ab33ab5e75bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MASE: 1.2699755793269745\n"
          ]
        }
      ],
      "source": [
        "print(f\"MASE: {np.mean(mase_metrics)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "w6ETpUrML2wE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w6ETpUrML2wE",
        "outputId": "7bc31d75-6194-498f-8b4b-4908aadf1d7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sMAPE: 0.15802448291491783\n"
          ]
        }
      ],
      "source": [
        "print(f\"sMAPE: {np.mean(smape_metrics)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "S91QglbEL7Qm",
      "metadata": {
        "id": "S91QglbEL7Qm"
      },
      "source": [
        "We can also plot the individual metrics of each time series in the dataset and observe that a handful of time series contribute a lot to the final test metric:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb342aac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "cb342aac",
        "outputId": "76ded197-eb9e-45b3-dbdf-070398b2f0c4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(mase_metrics, smape_metrics, alpha=0.3)\n",
        "plt.xlabel(\"MASE\")\n",
        "plt.ylabel(\"sMAPE\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "moksM2QmMACr",
      "metadata": {
        "id": "moksM2QmMACr"
      },
      "source": [
        "To plot the prediction for any time series with respect the ground truth test data we define the following helper:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ae05011",
      "metadata": {
        "id": "3ae05011"
      },
      "outputs": [],
      "source": [
        "import matplotlib.dates as mdates\n",
        "\n",
        "\n",
        "def plot(ts_index):\n",
        "    fig, ax = plt.subplots()\n",
        "\n",
        "    index = pd.period_range(\n",
        "        start=test_dataset[ts_index][FieldName.START],\n",
        "        periods=len(test_dataset[ts_index][FieldName.TARGET]),\n",
        "        freq=freq,\n",
        "    ).to_timestamp()\n",
        "\n",
        "    # Major ticks every half year, minor ticks every month,\n",
        "    ax.xaxis.set_major_locator(mdates.MonthLocator(bymonth=(1, 7)))\n",
        "    ax.xaxis.set_minor_locator(mdates.MonthLocator())\n",
        "\n",
        "    ax.plot(\n",
        "        index[-2 * prediction_length :],\n",
        "        test_dataset[ts_index][\"target\"][-2 * prediction_length :],\n",
        "        label=\"actual\",\n",
        "    )\n",
        "\n",
        "    plt.plot(\n",
        "        index[-prediction_length:],\n",
        "        np.median(forecasts[ts_index], axis=0),\n",
        "        label=\"median\",\n",
        "    )\n",
        "\n",
        "    plt.fill_between(\n",
        "        index[-prediction_length:],\n",
        "        forecasts[ts_index].mean(0) - forecasts[ts_index].std(axis=0),\n",
        "        forecasts[ts_index].mean(0) + forecasts[ts_index].std(axis=0),\n",
        "        alpha=0.3,\n",
        "        interpolate=True,\n",
        "        label=\"+/- 1-std\",\n",
        "    )\n",
        "    plt.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "mwtksAcxMHoK",
      "metadata": {
        "id": "mwtksAcxMHoK"
      },
      "source": [
        "For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5N8fdnm_MKQP",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "5N8fdnm_MKQP",
        "outputId": "471ce09b-d0fb-4035-da5f-49b5c2d8f0d5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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      "source": [
        "plot(334)"
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        "How do we compare against other models? The [Monash Time Series Repository](https://forecastingdata.org/#results) has a comparison table of test set MASE metrics which we can add to:\n",
        "\n",
        "|Dataset | \tSES| \tTheta | \tTBATS| \tETS\t| (DHR-)ARIMA| \tPR|\tCatBoost |\tFFNN\t| DeepAR | \tN-BEATS | \tWaveNet| \t**Transformer** (Our) |\n",
        "|:------------------:|:-----------------:|:--:|:--:|:--:|:--:|:--:|:--:|:---:|:---:|:--:|:--:|:--:|\n",
        "|Tourism Monthly | \t3.306 |\t1.649 |\t1.751 |\t1.526|\t1.589|\t1.678\t|1.699|\t1.582\t| 1.409\t| 1.574|\t1.482\t|  **1.256**|\n",
        "\n",
        "Note that, with our model, we are beating all other models reported (see also table 2 in the corresponding [paper](https://openreview.net/pdf?id=wEc1mgAjU-)), and we didn't do any hyperparameter tuning. We just trained the Transformer for 40 epochs. \n",
        "\n",
        "Of course, we need to be careful with just claiming state-of-the-art results on time series with neural networks, as it seems [\"XGBoost is typically all you need\"](https://www.sciencedirect.com/science/article/pii/S0169207021001679).  We are just very curious to see how far neural networks can bring us, and whether Transformers are going to be useful in this domain. This particular dataset seems to indicate that it's definitely worth exploring.\n",
        "\n",
        "## Next Steps\n",
        "\n",
        "We would encourage the readers to try out the [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) with other time series datasets from the [Hub](https://huggingface.co/datasets/monash_tsf) and replace the appropriate frequency and prediction length parameters. For your datasets, one would need to convert them to the convention used by GluonTS, which is explained nicely in their documentation [here](https://ts.gluon.ai/stable/tutorials/forecasting/extended_tutorial.html#What-is-in-a-dataset?). We have also prepared an example notebook showing you how to convert your dataset into the 🤗 datasets format [here](https://github.com/huggingface/notebooks/blob/main/examples/time_series_datasets.ipynb).\n",
        "\n",
        "As time series researchers will know, there has been a lot of interest in applying Transformer based models to the time series problem. The vanilla Transformer is just one of many attention-based models and so there is a need to add more models to the library.\n",
        "\n",
        "At the moment there is nothing stopping us from modeling multivariate time series, however for that one would need to instantiate the model with a multivariate distribution head. Currently, diagonal independent distributions are supported, and other multivariate distributions will be added. Stay tuned for a future blog post which will include a tutorial.\n",
        "\n",
        "Another thing on the roadmap is time series classification. This entails adding a time series model with a classification head to the library, for the anomaly detection task for example. \n",
        "\n",
        "The current model assumes the presence of a date-time together with the time series values, which might not be the case for every time series in the wild. See for instance neuroscience datasets like the one from [WOODS](https://woods-benchmarks.github.io/). Thus, one would need to generalize the current model to make some inputs optional in the whole pipeline.\n",
        "\n",
        "Finally, the NLP/Vision domain has benefitted tremendously from [large pre-trained models](https://arxiv.org/abs/1810.04805), while this is not the case as far as we are aware for the time series domain. Transformer based models seem like the obvious choice in pursuing this avenue of research and we cannot wait to see what researchers and practitioners come up with!\n"
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